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tf.contrib.factorization.gmm

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Creates the graph for Gaussian mixture model (GMM) clustering.

inp An input tensor or list of input tensors
initial_clusters Specifies the clusters used during initialization. Can be a tensor or numpy array, or a function that generates the clusters. Can also be "random" to specify that clusters should be chosen randomly from input data. Note: type is diverse to be consistent with skflow.
num_clusters number of clusters.
random_seed Python integer. Seed for PRNG used to initialize centers.
covariance_type one of "diag", "full".
params Controls which parameters are updated in the training process. Can contain any combination of "w" for weights, "m" for means, and "c" for covars.

Note tuple of lists returned to be consistent with skflow A tuple consisting of:
assignments A vector (or list of vectors). Each element in the vector corresponds to an input row in 'inp' and specifies the cluster id corresponding to the input.
training_op an op that runs an iteration of training.
init_op an op that runs the initialization.